metaNNvis 0.0.3

A meta-tool for cross-framework access to PyTorch and Tensorflow visualisation methods

MetaNNvis

MetaNNvis is a tool for accessing introspection methods for neural networks regardless of the framework in which the neural network has been built. It is easily extendable and currently supports models from TensorFlow 2.0 and PyTorch in combination with methods from Captum and tf-keras-vis. For more details, see the project report.

Installation

The latest version of metaNNvis can be installed via pip:

pip install metaNNvis

Additionally, you need the following dependencies: torch, seaborn, numpy, tensorflow, onnx2torch, onnx, captum, tf-keras-vis, torchvision, matplotlib and onnx2keras. All dependencies can be installed via pip except for onnx2keras, which can be downloaded from the project's GitHub page

Usage

For instructions on how to use cross-framework introspection and how to extend it by new methods, see the user guide.

Available methods

Cross-Framework Introspection currently supports most methods from Captum and all methods from tf-keras-vis. The supported methods are:

Method	Category
Captum
Integrated Gradients	primary, layer, neuron
Saliency	primary
DeepLift	primary, layer, neuron
GradientShap	primary, layer, neuron
Input X Gradient	primary
Gradient X Activation	layer
Deconvolution	primary, neuron
Feature Ablation	primary, layer, neuron
Feature Permutation	primary
Conductance	layer, neuron
Layer Activation	layer
GradCAM	layer
Neuron Gradient	neuron
tf-keras-vis
Activation Maximization	feature visualization
Vanilla Saliency / SmoothGrad	attribution
GradCAM	attribution
GradCAM++	attribution
ScoreCAM	attribution
LayerCAM	attribution

Currently not supported are:

Method	Category
Captum
DeepLiftShap	primary, layer, neuron
Guided Backpropagation	primary, neuron
Guided GradCAM	primary
Occlusion	primary
Shapley Value Sampling	primary
Lime	primary
KernelShap	primary
Layer Relevance Propagation	primary, layer
Internal Influence	layer